Systems and methods to recommend price of benefit items offered through a membership platform

ABSTRACT

Systems and methods are provided for recommending price of benefit items offered through a membership platform. Exemplary implementations may: obtain benefit information for content creators of a membership platform; obtain consumption information, the consumption information describing acceptance of offers for the benefit items at the requested amounts by the subscribers of the content creators; train a machine learning model on input/output pairs to generate a trained machine learning model, the individual input/output pairs including training input information and training output information; store the trained machine learning model; determine, using the trained machine learning model, recommended amounts of consideration for the benefit items that correspond to greater acceptance; generate recommendations for individual content creators conveying the recommended amounts for the benefit items offered by the individual content creators; and/or perform other operations.

FIELD

The disclosure relates to systems and methods to recommend price ofbenefit items offered through a membership platform.

BACKGROUND

Different platforms may be utilized by entities seeking contributionsfrom the general public to obtain a needed service(s) and/orresource(s). Some of these platforms facilitate raising resources (i.e.,funds) from the users through monetary contributions or donations tosupport a project. Oftentimes, supporters of a project are given rewardsor special perks, where the size and/or exclusivity of the rewards orspecial perks may depend on the amount contributed.

SUMMARY

A membership platform may be comprised of users including one or more ofcontent creators, subscribers, and/or other users. Content creators maybe users of the membership platform who offer content (also referred toas “benefit items”) to subscribers in exchange for consideration. A“benefit item” may refer to a good and/or service. A good may comprise aphysical good and/or a digital good. In some implementations,subscribers may donate funds to a content creator such that the benefititem may be the altruism in supporting the content creator. Subscribersmay be users of the membership platform who subscribe, through paymentof a one-time and/or recurring (e.g., monthly) fee, to one or morecontent creators. A subscriber of an individual content creator mayobtain access to benefit items offered through the membership platformby virtue of being a subscriber to the individual content creator. Asubscriber of an individual content creator may obtain preferentialaccess to benefit items offered through the membership platform byvirtue of being a subscriber to the individual content creator.Preferential access may refer to subscriber-only access to benefit itemsand/or other content. Preferential access may refer to tiered levels ofaccess to benefit items and/or other content. Different levels of accessmay offer different quantities, content types, and/or combinations ofbenefit items. Different levels may correspond to different amounts ofconsideration paid by the given subscriber. In some implementations,other users of the membership platform may obtain limited access tobenefit items. In some implementations, other users may be non-payingusers and/or one-time visitors to the membership platform.

Within a membership platform, content creators may not know how to pricetheir benefit items in order to obtain more paying subscribers. Thecontent creators would greatly benefit from a determination of optimalpricing for the different benefit items and/or types of benefit items.One or more implementations described herein may use machine learning,heuristics, and/or other techniques to determine optimal pricing.

One aspect of the present disclosure relates to a system configured forrecommending price of benefit items offered through a membershipplatform. The system may include one or more hardware processorsconfigured by machine-readable instructions. The computer components mayinclude one or more of a benefit component, a consumption component, amodel training component, recommendation component, and/or othercomputer components.

The benefit component may be configured to obtain benefit informationfor content creators of a membership platform, and/or other information.The benefit information may characterize benefit items offered by thecontent creators to subscribers of the membership platform in exchangefor requested amounts of consideration. By way of non-limitingillustration, the benefit information may characterize a first benefititem offered by a first content creator in exchange for a firstrequested amount of consideration.

The consumption component may be configured to obtain consumptioninformation and/or other information. The consumption information maydescribe acceptance of offers for the benefit items at the requestedamounts by the subscribers of the content creators. The acceptance ofthe offers for the benefit items by the subscribers may be describedbased on one or more of quantity of subscribers who have accepted theoffers at the requested amounts, amount of revenue received from theacceptance of the offers at the requested amounts, and/or other measure.

The model training component may be configured to train a machinelearning model to generate recommendations for recommended amounts ofconsideration for the benefit items that correspond to greateracceptance. The machine learning model may be trained on input/outputpairs to generate a trained machine learning model. The individualinput/output pairs may include training input information, trainingoutput information, and/or other information. The training inputinformation may include the benefit information and/or otherinformation. The training output information may include the consumptioninformation and/or other information. The model training component maybe configured to store the trained machine learning model.

The recommendation component may be configured to determine, using thetrained machine learning model, recommended amounts of consideration forthe benefit items that correspond to greater acceptance. By way ofnon-limiting example, a first recommended amount may be determined forthe first benefit item. The first recommended amount may be differentfrom the first requested amount.

The recommendation component may be configured to generaterecommendations for individual content creators conveying therecommended amounts for the benefit items offered by the individualcontent creators. By way of non-limiting example, a first recommendationconveying the first recommended amount may be generated for the firstcontent creator.

In some implementations, the recommendation component may be configuredto use the trained machine learning model to determine recommendedamounts of consideration for benefit items yet to be offered tosubscribers via the membership platform. By way of non-limitingillustration, the recommendation component may be configured to providethe trained machine learning model with benefit informationcharacterizing a second benefit item of a second content creator. Thetrained machine learning model may be configured to output recommendedamounts of consideration for the benefit items that correspond togreater acceptance. By way of non-limiting illustration, the model mayoutput a second recommended amount for the second benefit item.

As used herein, any association (or relation, or reflection, orindication, or correspondence) involving servers, processors, clientcomputing platforms, and/or another entity or object that interacts withany part of the system and/or plays a part in the operation of thesystem, may be a one-to-one association, a one-to-many association, amany-to-one association, and/or a many-to-many association or N-to-Massociation (note that N and M may be different numbers greater than 1).

As used herein, the term “obtain” (and derivatives thereof) may includeactive and/or passive retrieval, determination, derivation, transfer,upload, download, submission, and/or exchange of information, and/or anycombination thereof. As used herein, the term “effectuate” (andderivatives thereof) may include active and/or passive causation of anyeffect, both local and remote. As used herein, the term “determine” (andderivatives thereof) may include measure, calculate, compute, estimate,approximate, generate, and/or otherwise derive, and/or any combinationthereof.

These and other features, and characteristics of the present technology,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the invention. As usedin the specification and in the claims, the singular form of “a”, “an”,and “the” include plural referents unless the context clearly dictatesotherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example membership system.

FIG. 2 illustrates a system configured to recommend price of benefititems offered through a membership platform, in accordance with one ormore implementations.

FIG. 3 illustrates an example database.

FIG. 4 illustrates a method to recommend price of benefit items offeredthrough a membership platform, in accordance with one or moreimplementations.

FIG. 5 illustrates a user interface, in accordance with one or moreimplementations.

FIG. 6 . illustrates a user interface, in accordance with one or moreimplementations.

DETAILED DESCRIPTION

Some entities may seek to obtain funds through subscriptions. Suchentities may utilize online membership platforms that allow consumers tosign up for ongoing payments in exchange for rewards or other membershipbenefits. Entities seeking funding may be content creators, for example,artists, musicians, educators, etc. Content creators may create content,which may refer to one or more of information, experience, products,and/or other content provided to an audience or end-user, whether it bedigital, analog, virtual, and/or other form. For example, types ofcontent may include but is not limited to video content, podcasts,photographic art, webcomics, do-it-yourself crafts, digital music,performance art, and/or other types of content. Content creators mayutilize membership platforms that allow consumers to become subscribersof the content creator. As subscribers, consumers may contribute ordonate money to a content creator on a recurring (e.g., weekly ormonthly) basis and/or per piece of content created by the contentcreator. Content creators may interact with subscribers and/orprospective subscribers (e.g., consumers that show interest in thecontent created by content creators) in a variety of ways. Understandingthe price of benefit items which lead to greater acceptance bysubscribers is important in order to drive the content creators togreater growth.

FIG. 1 illustrates an example subscriber-based membership system 10(sometimes referred to herein as a “membership platform”). A contentcreator 12 may register and set up a creator account with subscriptionplatform 16. Content creator 12 may create a page on a website hosted byserver 22 of subscription platform 16 and input relevant information.Content creator 12 may input information associated with and/or relevantto content creator 12 via subscription component 18, such as creationinformation, content information, information specifying desired and/orinitial subscription levels, preferred revenue source information (e.g.,preferred currency, currency source, and/or other information),identification information (e.g., identification of applicable taxjurisdiction and/or other information), and/or other information. A pagecreated by content creator 12 may be built using such information tomake potential consumers aware of how content creator 12 may wish to besupported/receive support for his/her content creation in addition tosubscribership revenue. Content creator 12 may set up a content creatoraccount with subscription platform 16 through subscription component 18or another appropriate component allowing content creator 12 to registerwith subscription platform 16. Various types of information regardingcontent creator 12 may be input into subscription platform 16, some ofwhich may be information identifying content creator 12.

Consumer 14 (also referred to as a “subscriber”) may set up a subscriberaccount with subscription platform 16. In setting up the subscriberaccount, consumer 14 may input demographic information relevant toconsumer 14 (e.g., age, income, job, etc.). Information identifyingconsumer 14 (e.g., name, a picture, a phone number, etc.) may be inputby consumer 14 when setting up the subscriber account. Through the pagecreated by content creator 12, a consumer 14 may pledge to donate agiven amount of money to content creator 12 every time content creator12 creates content. For example, if content creator 12 is an artist,consumer 14 may pledge to donate ten dollars each time content creator12 creates a piece of art.

In order to remit payment to content creator 12, consumer 14 may set upa payment mechanism through subscription platform 16 as part of settingup his/her subscriber account. When subscription platform 16 is notifiedor determines that content creator 12 has created content, subscriptionplatform 16 may access payment network 26 to obtain and/or transfer thepledged amount from consumer bank 28 to content creator bank 30.Alternatively (or in addition to per content pledge donations), consumer14 may pledge to donate a given amount to content creator 12 on arecurring basis through subscription platform 16. For example, consumer14 may pledge to donate five dollars each month to content creator 12,where each month, subscription platform 16 may access payment network 26to obtain and transfer the pledged amount from consumer bank 28 tocontent creator bank 30. It should be understood that consumer 14 mayhave an established relationship with consumer bank 28, and that contentcreator 12 may have an established relationship with content creatorbank 30. It should be noted that subscription platform 16 may retain aportion, such as some percentage, of the pledged amount, as a fee forhosting the page created by content creator 12, providing paymentservices, etc.

As consideration for the pledged donations, content creator 12 mayprovide some type of preferential access to consumer 14 in the form of,e.g., special perks or rewards. Content creator 12 may specify tiers ofpreferential access based upon the amount of money consumer 14 pledgesto donate and/or depending on whether the pledged donation is arecurring donation or a per content donation. The amounts and/or typesof pledged donations that may be made by consumer 14 to back contentcreator 12 may be referred to as subscription levels.

For example, in return for a monthly, recurring dollar amount ofdonation, content creator 12 may provide a high-resolution digital imageof the artwork created during that month to consumer 14. In exchange fora weekly, recurring dollar amount of donation, content creator 12 mayprovide a high-resolution digital image of the artwork created duringthat month as well as a time-lapse video of content creator 12 creatingthe artwork. In exchange for another dollar amount per content donation,content creator 12 may provide a low-resolution digital image of theartwork. For another dollar amount per content donation, content creator12 may engage in a live webchat or live meet-and-greet with consumer 14.Various types of preferential access may be provided by content creator12 to consumer 14, and content creator 12 may specify the subscriptionlevel to preferential access correlation.

The preferential access may be provided to consumer 14 from contentcreator 12. For example, content creator 12 may email digital copies ofartwork to consumer 14 over a communications network, such as a localarea network (LAN), a wide area network (WAN), a wireless network (e.g.,WiFi), a mobile communication network, a satellite network, theInternet, fiber optic, coaxial cable, infrared, radio frequency (RF) orany other suitable network. The preferential access may be provided toconsumer 14 from content creator 12 via subscriber platform 16. Forexample, the live webchat between content creator 12 and consumer 14 maybe provided through some chat functionality of the page of contentcreator 12 hosted on server 22 of subscription platform 16, which mayreside on communications network 32 or on another network (not shown).

It should be noted that not all subscription levels are necessarilyassociated with preferential access. Some consumers may be driven tosubscribe to content creator 12 on the basis of created content ratherthan any special perks or rewards.

The specification and management of subscriptions on behalf of contentcreator 12 may be handled by subscription component 18 alone or inconjunction with database 24. For example, a user interface may beprovided via subscription component 18 allowing content creator 12 tospecify his/her desired subscription levels and correspondingpreferential access, as well as his/her preferred sources of revenue.Subscription component 18 may receive the information input by contentcreator 12 and transmit the information for storage as one or morerecords, matrices, or other data structures in database 24 or withinmemory local to subscription component 18. Database 24 or the localmemory of subscription co ponent 18 may be configured in a suitabledatabase configuration, such as a relational database, a structuredquery language (SQL) database, a distributed database, an objectdatabase, etc, Suitable configurations and database storage types willbe apparent to persons having skill in the relevant art.

Content creator 12 may add subscribership information, update and/ordelete existing subscribership information, add creation information, aswell as update and/or delete creation information, add, update, and/ordelete preferential access information and/or its correspondence tosubscription levels, etc. Such changes may be input via subscriptioncomponent 18 and reflected in its local memory and/or database 24. Itshould be understood that content creator 12 and/or consumer 14 may bean individual or some entity representative of an individual or group ofindividuals.

Apart from providing preferential access to consumer 14, content creatormay engage with consumer 14 by interacting in a variety of ways. Forexample, content creator 12 may communicate with consumer 14 over email,one or more social media platforms, a messaging platform or otherappropriate communication mechanism or method. It should be understoodthat such communication platforms or mechanisms may be embodied incommunications network 32 allowing content creator 12 and consumer 14 tocommunicate outside of subscription platform 16. It should be understoodthat communication platforms or mechanisms may operate in conjunctionwith subscription platform 16 such that one or more of their respectivefunctionalities may be utilized through subscription platform 16. Forexample, social media hyperlinks allowing information from contentcreator 12′s page may be provided on the webpage allowing contentcreator 12 to share content creation progress updates with consumer 14.For example, content creator 12 may respond to a communication fromconsumer 14 posted on a comment section provided on content creator 12′spage in a private message or as part of the comment thread. It should benoted that content creator 12 may engage a single consumer, e.g.,consumer 14, one-on-one and/or may engage a group of consumers. Forexample, content creator 12 may post a “public” comment on his/herwebpage that may be seen by any consumer that is a subscriber to contentcreator 12 and/or any consumer that may be a potential subscriber.

FIG. 2 illustrates a system 100 configured to recommend price of benefititems offered through a membership platform. In some implementations,system 100 may include one or more of server(s) 102, client computingplatform(s) 104, and/or other components. The terms client computingplatform, remote computing platform, and/or computing platform may beused interchangeably herein to refer to individual ones of the clientcomputing platform(s) 104. Server(s) 102 may be configured tocommunicate with one or more client computing platforms 104 according toa client/server architecture and/or other architectures via one or morenetwork(s) 122. . In some implementations, one or more network(s) 122may include the Internet and/or other networks. Client computingplatform(s) 104 may be configured to communicate with other clientcomputing platforms via server(s) 102 and/or according to a peer-to-peerarchitecture, a client-server architecture, and/or other architectures.Users may access system 100 via client computing platform(s) 104.

It is noted the system 100 of FIG. 2 may be the same as, or included aspart of, the system 10 shown in FIG. 1 . For example, the server(s) 102may be the same as or included in servers 22. Network(s) 122 may be thesame as or included in network 32. Individual client computing platformsof one or more client computing platforms 104 may be computing platformsutilized by content creator 12 and/or consumer 14 to access system 10and/or system 100. Non-transitory electronic storage 118 may be the sameas or included in database 24. Accordingly, those skilled in the artwill recognize that although system 10 and system 100 are shown anddescribed separately, they may comprise a single common system. However,in some implementations, the features and/or functionality of system 100may be provided remotely as a separate system from system 10.

Server(s) 102 may be configured by machine-readable instructions 106.Machine-readable instructions 106 may include one or more instructioncomponents. The instruction components may include computer programcomponents. The instruction components may include one or more of abenefit component 108, a consumption component 110, a model trainingcomponent 112, a recommendation component 114, and/or other instructioncomponents.

Benefit component 108 may be configured to obtain benefit informationfor content creators of a membership platform and/or other information.The benefit information may characterize benefit items offered by thecontent creators to subscribers of the membership platform in exchangefor requested amounts of consideration. The benefit items may becharacterized by different benefit types of the benefit items, and/orother characteristics. The benefit types may characterize the benefititems based on one or more of an amount of consideration requested toaccess the benefit items (e.g., the subscription level needed to obtainaccess to a given benefit item), a medium of creation, content of thebenefit items, and/or other characteristics. Medium of creation mayinclude one or more of physical, digital, and/or other mediums. Thecontent of the benefit items may include one or more of a painting, asong, music, spoken word audio (e.g., a podcast, a shout out, and/orother audio), a video content, photographic art, webcomics,do-it-yourself crafts, performance art, and/or other content. In someimplementations, benefit information may be obtained from individualcontent creator through entry and/or selection of the benefitinformation by the content creators into a user interface.

By way of non-limiting example, the benefit information may characterizea first benefit item offered by a first content creator in exchange fora first requested amount of consideration. The first benefit item may becharacterized by a first benefit type and/or other characteristics.

Consumption component 110 may be configured to obtain consumptioninformation and/or other information. The consumption information maydescribe acceptance of offers for the benefit items at requested amountsby the subscribers. The acceptance of the offers for the benefit itemsby the subscribers may be described based on one or more of quantity ofsubscribers who have accepted the offers at the requested amounts (e.g.,including one or more of a total, an average, a frequency, a quantityover a certain time period, etc.), an amount of revenue received fromsubscribers who have accepted the offers at the requested amounts (e.g.,including one or more of a total, an average, a frequency, a quantityover a certain time period, etc.), and/or other information.

Model training component 112 may be configured to train a machinelearning model to determine recommended amounts of consideration for thebenefit items that correspond to greater acceptance. The model trainingcomponent 112 may be configured to train the machine learning modelbased on a training corpus and/or other information to generate atrained machine learning mode. The training corpus may includeinput/output pairs. The individual input/output pairs may include one ormore of training input information, training output information, and/orother information. The training input information may include benefitinformation and/or other information. The training output informationmay include consumption information and/or other information. Modeltraining component 112 may be configured to store the trained machinelearning model.

The machine learning model may include one or more of a neural network,a convolutional neural network, and/or other machine-learning framework.In some implementations, the machine learning model may be configured tooptimize objective functions. In some implementations, optimizingobjective functions may include one or both of maximizing a likelihoodof the training set or minimizing a classification error on a held-outset.

Recommendation component 114 may be configured to determine, using thetrained machine learning model, recommended amounts of consideration forthe benefit items that correspond to greater acceptance. The conditionof “greater” may be relative to one or more of other benefit itemsoffered by other content creators (e.g., a system-wide basis), otherbenefit items offered by individual content creators (e.g., a contentcreator basis), and/or other information. In some implementations, therecommended amounts of consideration for the benefit items may representthe requested amounts of consideration for the benefit items having thelargest quantity of subscribers who have accepted the offers for thebenefit items at the requested amounts. In some implementations, therecommended amounts of consideration for the benefit items may representthe requested amounts of consideration for the benefit items havingreturned the highest revenue from the acceptance of the offers at therequested amounts.

In some implementations, the recommended amounts of consideration forthe benefit items may be characterized and/or categorized by benefittype. By way of non-limiting example, a first recommended amount may bedetermined for the first benefit item. By virtue of the first benefititem being the first benefit type, the first recommended amount may bedetermined for benefit items of the first benefit type.

Recommendation component 114 may be configured to generaterecommendations for individual content creators conveying therecommended amounts for the benefit items offered by the individualcontent creators. By way of non-limiting example, a first recommendationconveying the first recommended amount may be generated for the firstcontent creator.

In some implementations, the recommendation component 114 may beconfigured to provide the trained machine learning model with benefitinformation characterizing one or more benefit items of one or morecontent creators not included in the training corpus. By way ofnon-limiting illustration, the trained machine learning model may beused during registration of user accounts of the content creators withthe membership platform in order to initially set prices. The trainedmachine learning model may be configured to output recommendations forrecommended amounts of consideration. By way of non-limitingillustration, the recommendation component 114 may be configured toprovide the trained machine learning model benefit informationcharacterizing a second benefit item offered by a second contentcreator. The benefit information characterizing the second benefit itemoffered by a second content creator may be information not included inthe training corpus. The trained machine learning model may output asecond recommendation for a second recommended amount of considerationfor the second benefit item.

In some implementations, recommendation component 114 may be configuredto generate recommendations for individual content creators conveyingthe recommended amounts for the benefit items of a certain benefit type.The benefit types may characterize the benefit items based on one ormore of an amount of consideration requested to access the benefit items(e.g., the subscription level needed to obtain access to a given benefititem), a medium of creation, content of the benefit items, and/or othercharacteristics. By way of non-limiting illustration, a third benefititem and/or other benefit items may be of the first benefit type. Basedon the first benefit item being the first benefit type, the firstrecommended amount may be determined for the benefit items of the firstbenefit type. A third recommendation may be generated conveying thefirst recommended amount for the third benefit item by virtue of thethird benefit also being of the first benefit type.

In some implementations, recommendation component 114 may be configuredto effectuate presentation of the recommendations on a user interfacedisplayed on computing platform(s) 104 associated with the contentcreators. The user interface may be configured to obtain user entryand/or selection by the content creators to accept the recommendations.In some implementations, the user interface may be part of themembership platform. In some implementations, the user interface may beprovided through one or more communication channels external to themembership platform, e.g., email, SMS, etc. Acceptance of therecommendations may cause the requested amounts of consideration for thebenefit items to be automatically changed to the recommended amounts ofconsideration for the benefit items. An instance of a user interface mayinclude one or more user interface elements configured to facilitateuser interaction with the user interface. By way of non-limitingillustration, user interface elements may include one or more of textinput fields, drop down menus, check boxes, display windows, virtualbuttons, and/or other elements configured to facilitate userinteraction.

In some implementations, recommendation component 114 may be configuredto effectuate presentation of one or more recommendations forrecommended amounts of consideration for benefit items on a userinterface displayed on the computing platform(s) 104 associated withcontent creators during registration of user accounts within themembership platform. The user interface may be configured to obtain oneor more of user entry and/or selection by the content creators ofbenefit information, user entry and/or selection to acceptrecommendations, and/or other input. Acceptance of the recommendationsmay cause amounts of consideration for the benefit items to beautomatically set within the membership platform to the recommendedamounts of consideration.

By way of non-limiting illustration, recommendation component 114 may beconfigured to effectuate presentation of the second recommendation on auser interface displayed on a computing platform associated with thesecond content creator during a registration of a user account of thesecond content creator with the membership platform. The user interfacemay be configured to obtain user entry and/or selection by the secondcontent creator to accept the second recommendation. Acceptance of thesecond recommendation may cause an amount of consideration for thesecond benefit item to be automatically set within the membershipplatform to the second recommended amount of consideration.

FIG. 5 illustrates a user interface 500, in accordance with one or moreimplementations. The user interface 500 may display a recommendation 502conveying a recommended amount of consideration for a benefit itemoffered by a content creator. The user interface 500 may be configuredto obtain user entry and/or selection by the content creator to acceptthe recommendation 502. By way of non-limiting illustration, a userinterface element 504 may be provided. An acceptance of therecommendation 502 may cause a requested amount of consideration for thebenefit item to be automatically changed to the recommended amount ofconsideration.

FIG. 6 . illustrates a user interface 600, in accordance with one ormore implementations. The user interface 600 may display arecommendation 602 conveying a recommended amount of consideration for abenefit item to be offered by a content creator. The user interface 600may be displayed during a registration of a user account of the contentcreator with the membership platform. The user interface 600 may beconfigured to obtain user entry and/or selection by the content creatorto accept the recommendation 602. By way of non-limiting illustration, auser interface element 604 may be provided. An acceptance of therecommendation 602 may cause an amount of consideration for the benefititem to be automatically set to the recommended amount of consideration.

In some implementations, server(s) 102, client computing platform(s)104, and/or external resources 116 may be operatively linked via one ormore electronic communication links. For example, such electroniccommunication links may be established, at least in part, via a networksuch as the Internet and/or other networks. It will be appreciated thatthis is not intended to be limiting, and that the scope of thisdisclosure includes implementations in which server(s) 102, clientcomputing platform(s) 104, and/or external resources 116 may beoperatively linked via some other communication media.

A given client computing platform 104 may include one or more processorsconfigured to execute one or more computer program components. Thecomputer program components may be configured to enable an expert oruser associated with the given client computing platform 104 tointerface with system 100, system 10, and/or external resources 116,and/or provide other functionality attributed herein to client computingplatform(s) 104. By way of non-limiting example, the given clientcomputing platform 104 may include one or more of a desktop computer, alaptop computer, a handheld computer, a tablet computing platform, aNetBook, a Smartphone, a gaming console, and/or other computingplatforms.

External resources 116 may include sources of information outside ofsystem 100, external entities participating with system 100, and/orother resources. In some implementations, some or all of thefunctionality attributed herein to external resources 116 may beprovided by resources included in system 100.

Server(s) 102 may include electronic storage 118, one or more processors120, and/or other components. Server(s) 102 may include communicationlines, or ports to enable the exchange of information with a networkand/or other computing platforms. Illustration of server(s) 102 in FIG.2 is not intended to be limiting. Server(s) 102 may include a pluralityof hardware, software, and/or firmware components operating together toprovide the functionality attributed herein to server(s) 102. Forexample, server(s) 102 may be implemented by a cloud of computingplatforms operating together as server(s) 102.

Electronic storage 118 may comprise non-transitory storage media thatelectronically stores information. The electronic storage media ofelectronic storage 118 may include one or both of system storage that isprovided integrally (i.e., substantially non-removable) with server(s)102 and/or removable storage that is removably communicable withserver(s) 102 via, for example, a port (e.g., a USB port, a firewireport, etc.) or a drive (e.g., a disk drive, etc.). Electronic storage118 may include one or more of optically readable storage media (e.g.,optical disks, etc.), magnetically readable storage media (e.g.,magnetic tape, magnetic hard drive, floppy drive, etc.), electricalcharge-based storage media (e.g., EEPROM, RAM, etc.), solid-statestorage media (e.g., flash drive, etc.), and/or other electronicallyreadable storage media. Electronic storage 118 may include one or morevirtual storage resources (e.g., cloud storage, a virtual privatenetwork, and/or other virtual storage resources). Electronic storage 118may store software algorithms, information determined by processor(s)120, information received from server(s) 102, information received fromclient computing platform(s) 104, and/or other information that enablesserver(s) 102 to function as described herein.

Processor(s) 120 may be configured to provide information processingcapabilities in server(s) 102. As such, processor(s) 120 may include oneor more of a digital processor, an analog processor, a digital circuitdesigned to process information, an analog circuit designed to processinformation, a state machine, and/or other mechanisms for electronicallyprocessing information. Although processor(s) 120 is shown in FIG. 2 asa single entity, this is for illustrative purposes only. In someimplementations, processor(s) 120 may include a plurality of processingunits. These processing units may be physically located within the samedevice, or processor(s) 120 may represent processing functionality of aplurality of devices operating in coordination. Processor(s) 120 may beconfigured to execute components 108, 110, 112 and/or 114, and/or othercomponents. Processor(s) 120 may be configured to execute components108, 110, 112 and/or 114, and/or other components by software; hardware;firmware; some combination of software, hardware, and/or firmware;and/or other mechanisms for configuring processing capabilities onprocessor(s) 120. As used herein, the term “component” may refer to anycomponent or set of components that perform the functionality attributedto the component. This may include one or more physical processorsduring execution of processor readable instructions, the processorreadable instructions, circuitry, hardware, storage media, or any othercomponents.

It should be appreciated that although components 108, 110, 112 and/or114 are illustrated in FIG. 2 as being implemented within a singleprocessing unit, in implementations in which processor(s) 120 includesmultiple processing units, one or more of components 108, 110, 112and/or 114 may be implemented remotely from the other components. Thedescription of the functionality provided by the different components108, 110, 112 and/or 114 described below is for illustrative purposes,and is not intended to be limiting, as any of components 108, 110, 112and/or 114 may provide more or less functionality than is described. Forexample, one or more of components 108, 110, 112 and/or 114 may beeliminated, and some or all of its functionality may be provided byother ones of components 108, 110, 112 and/or 114. As another example,processor(s) 120 may be configured to execute one or more additionalcomponents that may perform some or all of the functionality attributedbelow to one of components 108, 110, 112 and/or 114.

FIG. 3 illustrates elements that may make up database 24. As indicatedpreviously, subscription component 18 of FIG. 1 may transmit informationinput by content creator 12 and/or consumer 14 regarding creation and/orsubscribership information to database 24. Subscription platform 16, viaserver 22, for example, may monitor and obtain creation and/orsubscribership information for storage in database 24. For example,subscription platform 16 may monitor and store additional contentcreated and/or subscriber demographic information as well asperformance-related subscribership information, e.g., engagementactivity between content creator 12 and his/her subscribers, one of whommay be consumer 14. For example, subscription platform 16 may monitorthe amount of money being generated and/or lost through the subscribers(e.g., outcome information), as well as content creator 12′s subscriberretention rate. For example, subscription platform 16 may monitor andstore performance-related creation information, such as the amount ofcontent that content creator 12 is creating, how often and/or howquickly content creator 12 reacts to subscriber engagement activity,etc.

Database 24 may include one or more databases or partitions in whichinformation relating to content creator 12, and/or subscribershiprelevant to content creator 12. For example, database 24 may include acontent creator database 24a, a content database 24b, a subscriberdatabase 24c, and a subscription database 24d. It should be noted thatthe elements and/or functionality of database 24 may be implemented inlocal memory resident in subscription component 18 or shared betweendatabase 24 and the local memory of subscription component 18 ratherthan solely in database 24.

Database 24 may be populated with one or more of benefit information,consumption information, recommendations, creation data and/orsubscription level information monitored or obtained from and/orassociated with existing content creator and/or subscriber accountsestablished in subscription platform 16, and/or other information.Creation data may refer to information that characterizes one or more ofcontent creator 12, the content that content creator 12 creates, andactivity engaged in by content creator 12 to interact with one or moresubscribers and/or to which consumer 14 is granted preferential access.

Content creator information characterizing content creator 12 may beinformation reflecting the type of creator that content creator 12designates him/herself to be and/or other self-identified preferencesregarding subscription offerings by content creator 12. For example,content creator type information may reflect that content creator 12 maybe a paint artist, a digital artist, a sculptor, a video game developer,a writer, a performance artist, etc. Content creator preferenceinformation may reflect subscription levels content creator 12 wishes tooffer to subscribers. Content creator preference information mayreflect, e.g., a desired minimum revenue, preferred sources of revenue,subscription level proportions, etc. For example, content creatorpreference information may include information indicating contentcreator 12′s desire for more subscribers pledging some amount of moneyor less subscribers pledging a greater amount of money. For example,content creator preference information may include informationspecifying that content creator 12 wishes to supplement his/hersubscription-generated revenue with revenue generated from the sale ofpromotional merchandise. Such information may be stored in a contentcreator database 24 a.

In addition to content creation-related information, and uponregistering with subscription platform 16 as a content creator, contentcreator 12 may input information characterizing the identity of contentcreator 12. For example, content creator 12 may input or upload contactinformation, a telephone number associated with a personal user device,such as a smartphone, an email address, a photograph, and/or otheridentifying information. Such identifying information may be used bysubscription platform 16 in a variety of ways to associate contentcreator 12 with particular content, his/her webpage, payment ofsubscription donations, and/or other information.

Content information characterizing the content that content creator 12creates may refer to one or more of the type of content created, themedium in which the content is created and/or presented (based onself-identified preference, commonality, and/or other measure), theamount of content created, and/or the frequency at which the content iscreated. For example, type of content information and/or content mediuminformation may indicate that content creator 12 prefers to createpaintings on canvas, develops video games for a mobile platform,performs in online musical performances, and/or other information. Forexample, content amount information may reflect that content creator 12created a series of artwork comprising four paintings. For example,content frequency information may indicate that content creator 12developed three video games over the course of six months. Suchinformation may be stored in content database 24 b.

Consumer 14 may subscribe to content creator 12 by registering withsubscription platform 16. During registration, consumer 14 may inputcertain subscriber demographic information indicative of economic and/orsocial characteristics of consumer 14. Subscriber demographicinformation may reflect the yearly income of consumer 14, a geographicarea in which consumer 14 resides, the age of consumer 14, interests ofconsumer 14, etc. Subscriber information may include data regarding theamount of money consumer 14 is currently pledged to donate to one ormore content creators. Over time, as monitored and collected bysubscription platform 16, subscriber information may include informationregarding the amount of money consumer 14 has previously donated to oneor more content creators, including content creator 12. Subscriberinformation, as monitored and obtained by subscription platform 16 mayinclude an Internet Protocol (IP) address indicative of a currentlocation of consumer 14 and/or an IP address indicating a paymentsource. Such information may be stored in subscriber database 24 c.

Like content creator 12, consumer 14 may input or upload otheridentifying information that may be used by subscription platform 16 ina variety of ways to associate consumer 14 with particular content, aparticular content creator, payment of subscription donations, etc. Forexample, a photograph or phone number of consumer 14 may be used, e.g.,as a mechanism for correlating consumer 14′s attendance at a live eventwith consumer 14′s status as a subscriber of content creator 12, anothercontent creator present at the live event, a subscriber of contentsimilar to that being presented at the live event, etc. Such informationmay be stored in subscriber database 24 c. Subscription component 18 oranother component may be used to provide a user interface that may beused by consumer 14 to input such information.

Subscription level information may refer to information characterizingdifferent subscription levels and corresponding preferential accessinformation specified by content creator 12. For example, subscriptionlevel information may reflect that a ten dollar recurring donation isrewarded with a high-resolution digital image of artwork created duringthat month to consumer 14. Such subscriber level information may bestored in subscription database 24 d.

It should be noted that other databases or partitions may make updatabase 24. For example, database 24 may include one or more databasesor partitions for storing information including, but not limited to thefollowing: preferential access information characterizing activity inwhich content creator 12 engagements may refer to data reflecting thetype of activity, the level and/or exclusivity of preferential access tothat activity granted to consumer 14; subscriber and/or content creatorengagement information characterizing interactions, the type and/orfrequency of interactions between subscribers and content creators,and/or the medium over which interactions may occur; and historicalsubscription level and/or engagement information reflecting subscriptionlevel and/or engagement information monitored and gathered over one ormore periods of time.

It should be noted that some of the information described above may notnecessarily be required. It should be noted that information reflectingadditional aspects of, e.g., the content, content creator, contentcreator preferences, and/or subscribership, is contemplated by thedisclosure. For example, preferential access need not necessarily beoffered for each subscription level. For example, subscriber data mayinclude data reflecting particular content creators to which asubscriber pledges donations.

FIG. 4 illustrates a method 400 to recommend price of benefit itemsoffered through a membership platform, in accordance with one or moreimplementations. The operations of method 400 presented below areintended to be illustrative. In some implementations, method 400 may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of method 400 are illustrated in FIG.4 and described below is not intended to be limiting.

In some implementations, method 400 may be implemented in one or moreprocessing devices (e.g., a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information). The one or moreprocessing devices may include one or more devices executing some or allof the operations of method 400 in response to instructions storedelectronically on an electronic storage medium. The one or moreprocessing devices may include one or more devices configured throughhardware, firmware, and/or software to be specifically designed forexecution of one or more of the operations of method 400.

An operation 402 may include obtaining benefit information for contentcreators of a membership platform. The benefit informationcharacterizing benefit items offered by the content creators tosubscribers of the membership platform in exchange for requested amountsof consideration. Operation 402 may be performed by one or more hardwareprocessors configured by machine-readable instructions including acomponent that is the same as or similar to benefit component 108, inaccordance with one or more implementations.

An operation 404 may include obtaining consumption information. Theconsumption information may describe acceptance of offers for thebenefit items at the requested amounts by the subscribers of the contentcreators. Operation 404 may be performed by one or more hardwareprocessors configured by machine-readable instructions including acomponent that is the same as or similar to consumption component 110,in accordance with one or more implementations.

An operation 406 may include training a machine learning model oninput/output pairs to generate a trained machine learning model. Theindividual input/output pairs including training input information andtraining output information. Operation 406 may be performed by one ormore hardware processors configured by machine-readable instructionsincluding a component that is the same as or similar to model trainingcomponent 112, in accordance with one or more implementations.

An operation 408 may include storing the trained machine learning model.Operation 408 may be performed by one or more hardware processorsconfigured by machine-readable instructions including a component thatis the same as or similar to model training component 112, in accordancewith one or more implementations.

An operation 410 may include determining, using the trained machinelearning model, recommended amounts of consideration for the benefititems that correspond to greater acceptance. Operation 410 may beperformed by one or more hardware processors configured bymachine-readable instructions including a component that is the same asor similar to recommendation component 114, in accordance with one ormore implementations.

An operation 412 may include generating recommendations for individualcontent creators conveying the recommended amounts for the benefit itemsoffered by the individual content creators. Operation 412 may beperformed by one or more hardware processors configured bymachine-readable instructions including a component that is the same asor similar to recommendation component 114, in accordance with one ormore implementations.

Although the system(s) and/or method(s) of this disclosure have beendescribed in detail for the purpose of illustration based on what iscurrently considered to be the most practical and preferredimplementations, it is to be understood that such detail is solely forthat purpose and that the disclosure is not limited to the disclosedimplementations, but, on the contrary, is intended to covermodifications and equivalent arrangements that are within the spirit andscope of the appended claims. For example, it is to be understood thatthe present disclosure contemplates that, to the extent possible, one ormore features of any implementation can be combined with one or morefeatures of any other implementation.

What is claimed is:
 1. A system configured to recommend price of subscription levels offered by content creators through an online membership platform, the system comprising: one or more physical processors configured by machine-readable instructions to: train a machine learning model to generate a trained machine learning model, the machine learning model being trained based on subscribership information for content creators of an online membership platform, the content creators offering one or more levels of ongoing subscribership to consumers in exchange for requested amounts of recurring consideration, the subscribership information characterizing benefit items received by subscribers of the content creators in accordance with the one or more levels of ongoing subscribership, the subscribership information further describing acceptance of offers for the one or more levels of ongoing subscribership at the requested amounts of recurring consideration by sets of the subscribers subscribing to the content creators, the trained machine learning model being configured to output a recommended amount of recurring consideration for a first level of ongoing subscribership offered by a content creator that corresponds to greater acceptance by the subscribers in the sets of the subscribers, the recommended amount of recurring consideration being different from an amount initially offered by the content creator for the first level of ongoing subscribership; store the trained machine learning model; and responsive to acceptance of a recommendation by the content creator via input into a user interface: update the subscribership information to reflect the recommended amount of recurring consideration being a current amount requested in exchange for the first level of ongoing subscribership to the content creator; and provide the trained machine learning model the subscribership information as updated to refine the trained machine learning model.
 2. The system of claim 1, wherein the one or more physical processors are further configured by the machine-readable instructions to: generate the recommendation based on the output of the trained machine learning model; and effectuate presentation of the recommendation on the user interface displayed on a computing platform associated with the content creator, the user interface being configured to receive the input to accept the recommendation.
 3. The system of claim 2, wherein the acceptance of the recommendation causes a recommended amount of recurring consideration to be automatically set as the current amount requested in exchange for the first level of ongoing subscribership to the content creator.
 4. The system of claim 1, wherein the acceptance of the offers for the one or more levels of ongoing subscribership by the subscribers is described based on quantity of subscribers who have accepted the offers at the requested amounts of recurring consideration.
 5. The system of claim 4, wherein the recommended amount of recurring consideration is an amount associated with having a largest quantity of subscribers who have accepted the offers.
 6. The system of claim 1, wherein the benefit items are characterized by benefit type of the benefit items.
 7. The system of claim 6, wherein the recommended amount of recurring consideration is for the benefit items of a certain benefit type.
 8. The system of claim 7, wherein the certain benefit type corresponds to a medium of creation of the benefit items.
 9. A system configured to recommend price of subscription levels offered by content creators through an online membership platform, the system comprising: one or more physical processors configured by machine-readable instructions to: provide a trained machine learning model with benefit information for a content creator of an online membership platform, the content creator offering one or more levels of ongoing subscribership to consumers, the benefit information characterizing benefit items received by subscribers of the content creator in accordance with the one or more levels of ongoing subscribership, the trained machine learning model having been trained based on acceptance of offers by sets of subscribers subscribing to content creators at the one or more levels of ongoing subscribership for one or more requested amounts of recurring consideration; generate, using output of the trained machine learning model, a recommendation conveying a recommended amount of recurring consideration for a first level of ongoing subscribership to the content creator that corresponds to greater acceptance by the subscribers in the sets of subscribers; responsive to acceptance of the recommendation by the content creator via input into a user interface: automatically set an offered amount of recurring consideration for the first level of ongoing subscribership to the recommended amount of recurring consideration; and provide the trained machine learning model with information indicating that the offered amount of recurring consideration for the first level of ongoing subscribership has been automatically set to the recommended amount of recurring consideration to refine the trained machine learning model.
 10. The system of claim 9, wherein the one or more physical processors are further configured by the machine-readable instructions to: effectuate presentation of the recommendation on the user interface displayed on a computing platform associated with the content creator during a registration of a user account of the content creator with the online membership platform; and wherein the user interface is configured to obtain first input by the content creator to accept the recommendation.
 11. A method to recommend price of subscription levels offered by content creators through an online membership platform, the method comprising: training a machine learning model to generate a trained machine learning model, the machine learning model being trained based on subscribership information for content creators of an online membership platform, the content creators offering one or more levels of ongoing subscribership to consumers in exchange for requested amounts of recurring consideration, the subscribership information characterizing benefit items received by subscribers of the content creators in accordance with the one or more levels of ongoing subscribership, the subscribership information further describing acceptance of offers for the one or more levels of ongoing subscribership at the requested amounts of recurring consideration by sets of the subscribers subscribing to the content creators, the trained machine learning model being configured to output a recommended amount of recurring consideration for a first level of ongoing subscribership offered by a content creator that corresponds to greater acceptance by the subscribers in the sets of the subscribers, the recommended amount of recurring consideration being different from an amount initially offered by the content creator for the first level of ongoing subscribership; storing the trained machine learning model; and responsive to acceptance of a recommendation by the content creator via input into a user interface: updating the subscribership information to reflect the recommended amount of recurring consideration being a current amount requested in exchange for the first level of ongoing subscribership to the content creator; and providing the trained machine learning model the subscribership information as updated to refine the trained machine learning model.
 12. The method of claim 11, further comprising: generating the recommendation based on the output of the trained machine learning model; and effectuating presentation of the recommendation on the user interface displayed on a computing platform associated with the content creator, the user interface being configured to receive the input to accept the recommendation.
 13. The method of claim 12, wherein the acceptance of the recommendation causes the recommended amount of recurring consideration to be automatically set as the current amount requested in exchange for the first level of ongoing subscribership to the content creator.
 14. The method of claim 11, wherein the acceptance of the offers for the one or more levels of ongoing subscribership by the subscribers is described based on quantity of subscribers who have accepted the offers at the requested amounts of recurring consideration.
 15. The method of claim 14, wherein the recommended amount of recurring consideration is an amount associated with having a largest quantity of subscribers who have accepted the offers.
 16. The method of claim 11, wherein the benefit items are characterized by benefit type of the benefit items.
 17. The method of claim 16, wherein the recommended amount of recurring consideration is for the benefit items of a certain benefit type.
 18. The method of claim 17, wherein the certain benefit type corresponds to a medium of creation of the benefit items.
 19. A method to recommend price of subscription levels offered by content creators through an online membership platform, the method comprising: providing a trained machine learning model with benefit information for a content creator of an online membership platform, the content creator offering one or more levels of ongoing subscribership to consumers, the benefit information characterizing benefit items received by subscribers of the content creator in accordance with the one or more levels of ongoing subscribership, the trained machine learning model having been trained based on acceptance of offers by sets of subscribers subscribing to content creators at the one or more levels of ongoing subscribership for one or more requested amounts of recurring consideration; generating, using output of the trained machine learning model, a recommendation conveying a recommended amount of recurring consideration for a first level of ongoing subscribership to the content creator that corresponds to greater acceptance by the subscribers in the sets of subscribers; and responsive to acceptance of the recommendation by the content creator via input into a user interface: automatically setting an offered amount of recurring consideration for the first level of ongoing subscribership to the recommended amount of recurring consideration; and providing the trained machine learning model with information indicating that the offered amount of recurring consideration for the first level of ongoing subscribership has been automatically set to the recommended amount of recurring consideration to refine the trained machine learning model.
 20. The method of claim 19, further comprising: effectuating presentation of the recommendation on the user interface displayed on a computing platform associated with the content creator during a registration of a user account of the content creator with the online membership platform; and wherein the user interface is configured to obtain first input by the content creator to accept the recommendation. 